Infrared imaging for monitoring component performance

ABSTRACT

A system and method for enhancing inspections using infrared cameras through in-field displays and operator-assisted performance calculations. A handheld infrared imaging system typically includes an infrared camera having a programmed computer and an interactive user interface suitable for displaying images and prompting response and accepting input from the infrared camera operator in the field during an inspection. An operator may designate at least one thing of interest on a displayed infrared image; and the programmed computer may uses a performance algorithm to estimate performance associated with the thing of interest. The programmed computer may extract information or parameters from previously measured data. The programmed computer may vary the way in which it displays new measurements based on the information extracted from the stored data. One or more of the parameters extracted from the IR image may be adapted to provide an automated alert to the user.

CROSS REFERENCES TO RELATED APPLICATIONS

This application is a Continuation of currently pending and allowed U.S.patent application Ser. No. 12/411,530 filed Mar. 26, 2009, entitled“METHODS FOR INFRARED INSPECTION USING PROGRAMMED LOGIC.” ThisApplication and U.S. patent application Ser. No. 12/411,530 areContinuations of U.S. patent application Ser. No. 11/424,361 filed Oct.3, 2006, entitled “APPARATUS AND METHOD FOR INFRARED IMAGING WITHPERFORMANCE ALGORITHM,” granted as U.S. Pat. No. 7,528,372. This patentapplication claims priority from and is related to U.S. ProvisionalPatent Application Ser. No. 60/728,064 filed Oct. 19, 2005, entitled:“INFRARED IMAGING WITH PERFORMANCE ALGORITHM.” This application claims apriority date of Oct. 19, 2005, which is the effective filing date ofcurrently pending and allowed U.S. patent application Ser. No.12/411,530 filed Mar. 26, 2009, which claims priority from U.S. patentapplication Ser. No. 11/424,361 filed Oct. 3, 2006 and granted as U.S.Pat. No. 7,528,372, which claims priority from U.S. Provisional PatentApplication Ser. No. 60/728,064 filed Oct. 19, 2005. U.S. patentapplication Ser. Nos. 12/411,530, 11/424,361, and U.S. ProvisionalPatent Application Ser. No. 60/728,064 and U.S. Pat. No. 7,528,372 areincorporated by reference in their entirety herein.

FIELD

This invention relates to the field of inspection with infrared cameras.More particularly, this invention relates to infrared cameras forassessing performance characteristics of equipment.

BACKGROUND

Infrared imaging systems capture two dimensional information regardingelectromagnetic radiation that is emitted by objects in a designatedregion of the infrared spectrum over a field of view. Typically the twodimensional information is captured at a focal plane that isperpendicular to the optical axis of a focusing lens or mirror andpasses through the focal point of the lens or mirror. Techniques havebeen developed to display the information at the focal plane in avisible spectrum in a manner that indicates comparative levels ofinfrared emission intensity across the focal plane. Because warmerobjects radiate more infrared radiation and cooler objects radiate lessinfrared radiation, the displayed images provide an indication of thecomparative temperature of objects in the field of view.

Infrared imaging technology has developed very rapidly, particularlysince the widespread acceptance of uncooled focal plane array detectors.Imagers are now capable of detecting faulty steam traps, leakinghydrocarbon gases, water leaks in roofing, inadequate buildinginsulation, effect of friction from various causes, electrical faults,heat exchanger faults, fluid levels, etc. Problems and other findingsmay be detected by a skilled in-field operator using a modern infraredcamera appropriate to the task. Another important development has beenthe integration of computational systems, digital displays, andversatile user interaction devices into handheld or otherwise portableinfrared imaging systems like those disclosed Piety in U.S. Pat. No.5,386,117 (referred to hereinafter as '117), Warner in U.S. Pat. No.6,849,849 (referred to hereinafter as '849), Hamrelius in U.S. Pat. No.6,984,824 (referred to hereinafter as '824), and Garvey in US PatentApplication Publication US 20060017821 (referred to hereinafter as20060017821). However, there are problems with existing technology.These problems include:

-   Detection and documentation of one or more of the cited problems    result in an incomplete in-field solution.-   Set-up requirements to adequately interpret the signal are too    complex or require a higher level of end user knowledge.-   Manually configured evaluation parameters cannot be readily adapted    to changing conditions in the monitored asset.-   Inadequate methods to automatically compare current readings with    previous measured or stored readings.-   Inability to monitor an asset and detect significant variations from    previous measurements in an automated fashion.

What are needed are devices and methods for things like estimating thecost of not taking corrective action, defining aspects of the correctiveaction that are needed, and verifying that the corrective action wasaccomplished and desired performance improvement has been achieved.

SUMMARY

Various exemplary embodiments provide a portable apparatus forperforming in-field analysis of an object performing its function.Typically the system includes a portable computerized camera system forproducing analyzed infrared measured information corresponding at leastin part to the object. The system also generally incorporates a userinterface that is in communication with the computerized camera systemfor displaying an image, displaying at least one thing of interest onthe image, and displaying a result, where the computerized camera systemis programmed with additional information about the thing of interestand programmed with a performance algorithm that uses the analyzedinfrared information and additional information about the thing ofinterest to calculate a result corresponding to a performancedetermination associated with the object.

Some embodiments provide portable apparatus for performing in-fieldanalysis of an object performing a function. The system usually includesa computerized infrared camera system for producing an infrared image ofthe object, where the infrared image contains thermal informationcorresponding to the object, and where the computerized infrared camerasystem is programmed for analyzing the thermal information to produce aresult. Generally the system provides a user interface in communicationwith the computerized system for displaying the infrared image, forreceiving user input corresponding to the object in the thermal imageand for displaying the result. The computerized system is furtherprogrammed with additional information corresponding to the intendedfunction of the object and further programmed with a performancealgorithm that uses the thermal information, the user input and theadditional information to calculate the result corresponding to a levelof performance of the object in performing its function.

Methods are provided for analyzing the performance of an object in animage produced by a portable infrared imaging apparatus. Typically amethod includes a step of defining a thermal zone in the image, wherethe thermal zone has a boundary. A typical further step is to determinea first image expanse between a first point and a second point on theboundary of the thermal zone where the first point and the second pointare spaced apart a known reference distance. Typically in an additionalstep, a second image expanse is determined between a third point andfourth point spaced apart a first unknown distance in the image. Then ina further step, a first estimated distance between the third point andthe fourth point in the image is calculated using the known referencedistance and a ratio between the first image expanse and the secondimage expanse.

BRIEF DESCRIPTION OF THE DRAWINGS

Various advantages are apparent by reference to the detailed descriptionin conjunction with the figures, wherein elements are not to scale so asto more clearly show the details, wherein like reference numbersindicate like elements throughout the several views, and wherein:

FIG. 1 is a block diagram of an infrared camera system.

FIG. 2 is a schematic diagram of a display on a portable infrared camerasystem with items for dimensioning.

FIG. 3 is a depiction of a 3-dimensional reference thermal object thatmay be placed in the infrared image to provide both dimension andperspective.

FIG. 4 is a depiction of an image in a portable infrared camera showingtwo reference thermal objects.

FIG. 5 is a schematic top view of the cross section from a wall showingcomponents affecting heat loss calculations.

FIG. 6 is a schematic diagram of a wall with sagging insulation asviewed using a portable infrared camera system.

FIG. 7 is a somewhat schematic illustration of an infrared view of aroof with wet insulation as displayed on a portable infrared camerasystem.

FIG. 8 is a somewhat schematic illustration of a changing fire situationas displayed over time on a portable infrared camera system.

FIG. 9 is an illustration of a flashover line representation asdisplayed on a portable infrared camera system.

FIG. 10 is a somewhat schematic illustration of a rollover indication asdisplayed on a portable infrared camera system.

FIG. 11 is a table representing estimates displayed on a portableinfrared camera system for steam loss through a failed steam trap.

FIG. 12 is a somewhat schematic illustration of a gas leak as displayedon a portable infrared camera suitable for detecting those gases.

FIG. 13A depicts an infrared image of a motor inspected at a first dateusing a portable infrared camera system.

FIG. 13B depicts an infrared image of the motor of FIG. 13A inspected ata second date using a portable infrared camera system.

FIG. 13C depicts an infrared image of the motor of FIGS. 13A and 13Bthat has been enhanced by a performance algorithm and displayed on aportable infrared camera system.

FIG. 14 depicts a flow chart of a method embodiment.

DETAILED DESCRIPTION

Described herein are various embodiments of portable apparatuses forperforming in-field analysis of an object performing its intendedfunction. Also described are processes for analyzing the performance ofan object in an image produced by a portable infrared imaging apparatus.Many of the embodiments described herein involve performing calculationsin a transportable infrared imaging system to assist the operator withthe in-field inspection or surveillance processes, including possiblyshort-term continuous monitoring with live update. These techniquesapply to a range of inspection operations including the followingexamples: building insulation, furnace and boiler and refractorystructures, firefighting, power transmission and distribution,hydrocarbon leaks, valve operation, effects of mechanical friction, andother thermally responsive applications.

One element that is applied in most embodiments is a performancealgorithm. A performance algorithm uses analyzed infrared informationand additional information about a thing of interest to calculate aresult corresponding to a performance determination associated with thething of interest. Performance algorithms are distinguished from otheralgorithms which simply analyze infrared information without evaluationany non-thermal aspect of the object or its function. Results producedby such other algorithms may be a graphical representation of infrareddata, quantification of thermographic statistics, alarm conditions, orother conclusion based solely on temperature and emissitivitycharacteristics.

Examples of “analyzed infrared information” that may be used in aperformance algorithm includes absolute, differential, and relativetemperature measurements; temperature line-profiles and area-histogramsand hot-spot and cold-spot over space; infrared emissivity selection;thermal contour mapping; and infrared imagery with graphicalenhancement. The analyzed infrared information may also be a findingsuch as “steam trap failed” or “missing insulation”. Examples of“additional information” that may be used in a performance algorithminclude camera parameters such as effective lens focal length, focusdistance, fields of view, and minimum detectible limits with respect toa sensed object. Additional information may also include data regardingnormal and abnormal operating parameters (such as temperature,dimensions, insulating values, leak rates, thermal conductivity, energyconsumption, and for equipment being inspected. Additional informationmay also include physical parameters such as physical property constantsor variables. Additional information may also include typical or defaultor optional respective numerical values associated with algorithmicparameters.

The performance determination (i.e., the calculation result) produced bya performance algorithm includes an understood aspect of the object inits intended function. The “understood aspect” is typically an object ofthe infrared inspection. For example during a steam trap survey,understood aspects are those things observed and concluded by theoperator using the infrared imager that reveal the operating conditionof the trap: normal, failed open, failed closed, intermittent, or notoperating. For another example, the understood aspect may be a physicallaw such as a mathematical relationship between changing temperature andchanging electrical power or mechanical friction. For yet anotherexample the understood aspect may be an experience based calculation orrule of thumb determination such as concluding or assuming theinsulating R-value for a portion of a roof is diminished 70% because ofevidently water-logged insulation. In other words, the performancedetermination is an assessment of performance of the object consideringits intended function. Examples of performance determinations include,proportional dimensions, valve performance characteristics, gasemissions quantities, electrical fault characteristics, insulatingfunctionalities, fire characteristics, effects of friction, costestimations, rate of change and deviation from a baseline image.

Equipment and methods disclosed herein apply to widely variedapplications. Here are some example applications.

-   -   Furnace and boiler and refractory structure inspection—Provide        quantitative results regarding length, area, and associated        thermal aspects collected during the inspection. Use this        information in a performance calculation.    -   Valve operation—estimate the material or financial losses from        failed steam trap or other valve operations, calculate the cycle        time, characterize valve or trap as over-size or under-size or        okay.    -   Hydrocarbon leaks—estimate the volume of gas cloud due to        fugitive emissions, estimate the mass flow rate, estimate the        cost of the leak, estimate dimensions subject to spark ignition        possibilities.    -   Building inspection—Allow less sophisticated operators to        perform more valuable inspections using the algorithms in the        camera to estimate R-value, heat losses, and opportunities for        cost savings.    -   Firefighting—provide quantitative calculations regarding thermal        danger and safe zones, flame characteristics, and physical        dimensions or proportions.    -   Power transmission and distribution—estimate remaining time to        failure for an electrical connection based on temperature        increase, estimate efficiency for convection radiators.    -   Machinery health—estimate aspect(s) of mechanical friction.

Equipment and methods disclosed herein generally advance the state ofthe art by providing in-camera firmware, which is imbedded software,with a performance algorithm calculation or specific display algorithmsto assist the operator to perform his job more effectively andefficiently. It is in the field that the operator has the bestopportunity to postulate, understand, and explain things calculated foranalysis and reporting purposes. Various devices and methods disclosedherein help relieve the burden of reconstructing field observations inthe field when later attempting to evaluate performance aspects ofequipment observed in the field. Equipment and methods disclosed hereintypically enable the practical common use of analyses that previouslycould only be done in a time-consuming manner for relatively rarecircumstances which justify the use of in-office desk-top applicationsto perform such time consuming research and analysis. Exemplaryembodiments also provide the ability for the end user to establish liveinfrared monitoring of an image with automated analysis or alarmingbased on deviations from a previously measured image or set of images.In summary, many of the devices and methods disclosed herein provide asystem and method for enhancing inspections using infrared camerasthrough the use of in-field, operator-assisted performance calculationsand display techniques.

Some exemplary embodiments incorporate a handheld infrared imagingsystem which includes an infrared camera, a programmed computer, aninteractive user interface suitable for displaying images and promptingresponse and accepting input from the infrared camera operator in thefield during an inspection, wherein the user interface enables anoperator to designate at least one thing of interest on a displayedinfrared image; and the programmed computer uses a performance algorithmto estimate performance associated with the thing of interest such asone of the following: a dimensioned-measurement value or athermal-property characteristic or an electrical-property characteristicor a cash value or a rate-of-change characteristic or an assessment ofrisk or danger.

Certain embodiments employ a method for assisting the operator of ahandheld infrared camera to accomplish a performance calculationsupporting in-field analysis of an item of interest. For example, theoperator carries a programmed infrared camera into the field to performan inspection using infrared imaging. During the inspection the operatoridentifies a thing of interest. The operator actuates a user interfaceto select the thing of interest on the infrared image. The programmedinfrared camera then uses a programmed performance algorithm to estimateperformance associated with either the thing of interest or with otherobjects contained in the image.

Many embodiments use a modern infrared camera system capable ofdetecting and reporting problems of interest such as faulty steam traps,leaking hydrocarbon gases, water leaks in roofing, inadequate buildinginsulation, effect of friction from various causes, electrical faults,heat exchanger faults, fluid levels. Infrared imaging systems disclosedby Piety in U.S. Pat. No. 5,386,117, Warner in '849, Hamrelius in '824,and Garvey in US 20060017821, represent the diversity of handheld andother portable infrared imaging systems having four elementsschematically represented by FIG. 1, a typical infrared camera system(101) which includes internal computer having firmware and memory, userinterface (102), infrared imaging lens (103), and mechanism for easilytransporting the imager (104). The user interface (102) always includesat least a visual display to prompt the user and report calculatedperformance results. A system similar to that described by Warner reliesentirely on interacting with the user through the display so itsoperations must be preconfigured with little or no user input. Moresophisticated user interfaces like those described by Piety '117,Harmelius '849, and Garvey US 20060017821, enable the user to interact,providing inputs and responding to prompts using a display, a cursor, amouse or joystick, a touch screen, push buttons, a full keypad, amicrophone, and equivalent things, any of which are built into a camerabody, preferably, but which alternatively could be external. Byreviewing these four invention disclosures, one may understand thediverse user interface options that are available for establishingcommunications between a user in the field and the operating system of acomputerized infrared camera system.

Exemplary embodiments use one or more firmware performance algorithms toestimate a performance parameter derived at least in part usinginformation derived from an infrared image. Typically this involveseither the use of firmware to provide calculations in the field whichare used to by the operator to perform analysis or to create at least aportion of the reporting process while in the field and/or the use ofprevious measurements to optimize the display of a newly captured orlive image for identification of changes or fault conditions. Here aresome of many different ways that this may be implemented by ones skilledin relevant technical disciplines.

For example, one may calculate steam losses from a seam trap or othervalve application. An infrared imager is particularly good atnon-intrusively revealing information about steam trap operation. Insome instances a particular design trap is intended to be normally open.If this type trap fails in the closed position, the failure may bedetected using an infrared imaging camera and an appropriate calculationmay be made using best information available such as system pressure andpipe diameter. This calculation may be improved using measuredtemperatures, pressure, sonic decibel or ultrasonic decibel values,cycle-time, or other related measurements or observations.

As another example, one may calculate gas losses from leakinghydrocarbon gases. Certain infrared imagers are capable of imaging thepresence of leaking hydrocarbons such as methane, ethane, butane, etc.In this case the infrared imager appears to be detecting the absence ofinfrared radiation which has been absorbed by the gas after it wasemitted from a surface behind the gas. The imager is able to see thepresence and relative size of the plume emanating from a leak source. Aperformance algorithm may be employed to calculate the volume of gasesby one of several means. One way is to first determine the detectablehydrocarbon concentration. Then by scribing a contour line around thedetected gases originating from the leak, it is reasonable to assume agas concentration at that contour. Furthermore, it is conceivable thatthe diminishing radiation from behind the gas could also be used tocalculate the relative increase in gas concentration close to the leak.Additional ultrasonic monitoring may be used to enhance or validate aleakage calculation algorithm. There are also safety considerations andcalculations that may be employed considering the fuel to oxygen ratio.It is conceivable that one may estimate the damage that may be done if aspark were to ignite in a vicinity of the hydrocarbon leak and displaythis risk or danger to the operator through the infrared camera system.

As another example one may calculate things about electricalapplications. Infrared measurements and imaging may be used to detectthe presence of faults in electrical power, transmission, anddistribution systems. These faults tend to be progressive. By knowingthe temperatures and rate of change in temperature, one may estimateseverity, power loss, time-to-failure, etc. Exemplary embodimentstranslate delta-temperature measurements made using an infrared camerainto corresponding changes in electrical properties such as resistanceor other impedance that produces heat. There are safety considerationshere as well. Delta-temperature in these applications is often comparedto ambient and compared between similar components operating undersimilar conditions.

For another example one may approximate energy loss, as well as repairor upgrade or replacement costs using a combination of assumed, known,and measured information. Infrared imaging is used in buildinginspection for roofing, walls, windows, flooring, etc. These imagers arehighly sensitive picking up very small changes in temperature oremissivity due to moisture or various properties of insulation. Thesedevices reveal possible problems. Such embodiments add value byquantifying the impacts of these observations and displaying thatinformation to the inspector while in the field.

For another example one may calculate certain smoke or flame performanceparameters using infrared imaging. Certain infrared imagers are able toview objects through otherwise obscuring flame or smoke. One type imageis capable of inspecting the solid surfaces inside a boiler furnacewhile looking through flames exceeding 1000 degrees F. These imagers maydetect clinkers, fouling, buildup, coking, refractory damage, and manyother problems. One may use the information from the image to estimatethe length, area, size, and volume of the furnace affected by thedamage. That information may in turn be used in further calculations.Some imagers are able to report pertinent temperature informationregarding the surfaces. If this information is produced, then thermalmodels may be verified using algorithmic calculations in the imager. Adifferent kind of imager altogether is commonly used by firefighters tosee through smoke, to identify people and objects, and to see the sourceand temperature of flames. It is logical that these imagers will begreatly improved by performance algorithm calculations to assist thefirefighter in performing his or her diverse job functions. The imagermay be used to assist the firefighters in assessing risk and alerting ofdangerous conditions.

As another example one may calculate energy caused by mechanicalfriction. Mechanical energy losses due to friction translate into heat,noise, damage, and power transfer. By measuring the increasedtemperature due to friction, and assuming or measuring several otherthings about the component geometry and materials, one may estimate thefriction energy, friction forces, friction coefficient. In thisapplication, one normally compares temperatures to ambient and betweensimilar components operating under similar conditions.

These examples (steam traps and other valves, leaking hydrocarbon gases,electrical applications, building inspections, smoke and flameapplications, mechanical friction, etc.) illustrate just some of theways in which in-the-field performance algorithms may assist theinfrared inspector.

Infrared inspection using an on-board performance algorithm calculationmay be done one-time or many times. Sometimes it is particularlyvaluable to continue to perform inspections during the process of makingchanges or adjustments intended to improve performance. Furthermore itmay be useful to verify the effectiveness of performance changes afterrepairs or corrective actions are accomplished.

In some embodiments employing performance algorithms, personnelreviewing an image on the inspection camera may use software in theinspection camera to read, write, or respond to a note related to ameasurement location. Such note may include one or more of thefollowing:

-   -   a safety instruction (e.g. “use protective gear”, “stand minimum        3 ft. away from panel”)    -   a general procedural note (e.g. “use wide angle lens”)    -   a specific request for additional measurement type (e.g.        “collect image from side of box”, “zoom in on left breaker”)    -   a mandatory requirement for confirmation of an action taken by        the end user in the field, such as by choosing from a set of        predefined responses.        In such embodiments the note may include any combination of the        following media:    -   text    -   graphic    -   voice    -   video    -   URL or hypertext link        Application of the note may be specified as:    -   a forced display (i.e. pop-up) on next measurement survey    -   a forced display (i.e. pop-up) on every survey    -   an optional display (i.e. user initiated) on next measurement        survey    -   an optional display (i.e. user initiated) on every survey

When in the field, the user may have further ability to interact withthe messaging system. For example, when a forced “pop-up” message isconfigured for a mandatory confirmation, the end user would typically berequired to select from the predefined responses (e.g., “proper safetyprecautions implemented”, or “special instructions read andunderstood”). The user may have the option to acknowledge a forced“pop-up” message that is assigned to the next measurement survey so thatthe message does not appear on the next measurement survey. Variationsof this technique include, acknowledging a forced “pop-up” messageassigned to the next measurement survey so that it is retained and willappear on the following survey as well, acknowledging a forced “pop-up”message assigned to the next measurement survey so that it will bedisplayed again in preset number of minutes, accessing an optionalmessage on the next survey so that if no further action is taken themessage expires and will not be displayed again, and accessing anoptional message on the next survey and acknowledging it so that it willbe retained as an optional message for the following survey.

In some embodiments, two-way communication may be established between ahost computer, perhaps in the office, and the inspection camera in thefield. In such embodiments the actions undertaken by the user in thefield to interact with the messaging system are typically recorded andtransferred to the host computer so that a user in the office isinformed of the actions taken in the field. As a further enhancement insome embodiments, the end user in the field also has the ability todefine additional notes which may take any of the forms describedherein.

A live two-way communication link between a host computer and aninspection camera in the field may used for other purposes as well. Forexample, threshold values used to determine which pixels on a new (live)image are displayed in a distinguishable manner such as a differentiatedcolor scheme may be downloaded to the inspection camera in the field.Other features of the image displayed on the inspection camera as wellas elements of the performance algorithms used by the inspection camerain the field may also be controlled by the host computer using thetwo-way communication link

The process of using a portable imaging apparatus having a field of viewto analyze the performance of an object generally involves a number ofsteps. The analysis may include, or may entirely consist of, the use ofa performance algorithm to calculate a dimensional performancecharacteristic of an object. In such a performance algorithm, one steptypically involves determining a dimension of a first thermal zone in animage produced by the portable imaging apparatus. A thermal zone is anarea of an image that has a common thermal property, and is bordered atleast in part by an area of the image that has a different thermalproperty. For example, a thermal zone may be a first area of an imagethat represents temperatures ranging between 100° C. and 120° C., and atleast a part of that thermal zone is bordered by a second area of theimage that represents temperature less than 100° C. or greater than 120°C. Note that a thermal zone may or may not represent the shape of anobject in an image. Often an infrared image of an object does notcorrelate geometrically very well with an image of the same objectcaptured in the visible light spectrum.

Often a process involving proportional dimensions (also furtherdescribed later herein) is then used in the performance algorithm tocalculate a second dimension in the image. The process of usingproportional dimensions often involves determining a first image expansebetween a first point and a second point on the boundary of a thermalzone, where the first point and the second point are spaced apart aknown reference distance. The term “image expanse” refers to a physicalfeature of the image, such as a pixel count, a subtended arc, or similardimension. The process also generally involves determining a secondimage expanse between a third point and fourth point spaced apart afirst unknown distance in the image. The second image expanse may bebetween two points on the same thermal zone, or two points on adifferent thermal zone. The process then typically proceeds withcalculating an estimated distance between the third point and the fourthpoint in the image using the known reference distance and a ratiobetween the first image expanse and the second image expanse. In someembodiments dimensional adjustments for visual perspective may be usedto enhance the accuracy of the calculation of the second dimension. Theestimated distance may be a dimensional performance characteristic thatby itself is an important performance characteristic of an object. Forexample, the length of a fire plume in a natural gas flame may be aperformance characteristic of interest to an equipment operator. In someembodiments a dimensional performance algorithm may include the use ofmultiple dimensions to calculate an area on the image.

In many embodiments, another step in the process of using a portableimaging apparatus to analyze the performance of an object involves usinga thermal performance algorithm to calculate a thermal performancecharacteristic, such as calculating an average temperature, calculatinga temperature difference, calculating a thermal gradient, defining anisotherm, or deriving some other temperature-related parameterassociated with an object in the image. In some embodiments the thermalperformance algorithm involves calculation of a trend over time as athermal performance characteristic.

In exemplary embodiments, the result of the thermal analysis may be usedat least in part to calculate further performance characteristics of anobject in the image. Sometimes this calculation combines dimensional(length or area or volume or circumference) information from the imagewith thermal information from the image. Sometimes the calculation ofsuch a performance characteristic utilizes additional information, thatis, information beyond information that may be discernable from theimage. Such additional information may include mechanical, electrical,or fluid characteristics of an object in the image. For example, if theobject in the image is an electrical component, the additionalinformation may be the current flowing through the electrical component,and the performance characteristic that is calculated may be theresistance of the electrical component. If the object in the image is amechanical component, the additional information may be force andvelocity vectors, and the performance characteristic that is calculatedmay be the mechanical friction of the object.

In some embodiments using a portable imaging apparatus to analyze theperformance of an object, a calculated performance characteristic iscompared against an alarm level to potentially generate an automatedalert. The alarm level may be defined in the host computer anddownloaded into the portable imaging apparatus.

Some embodiments described herein employ performance algorithms havingmany known and unknown variables. Typically, values for only some ofthese variables are perceived or measured in the field by the operatorusing the infrared camera. In the foregoing and following examples, someof the physical property values and constants or variables must beassumed or looked up from a table or other reference source. In somecases the firmware is programmed to insert default values for these. Inother cases, the user is prompted to insert a value or select from alist of various values or make some other selection that logicallydetermines the missing values. Many times the selection of the unknownvariables and other material properties is a best guess or compromiseoften decided by the engineers who designed the firmware logic. Here aresome examples of particularly useful performance algorithms.

EXAMPLE 1 Proportional Dimensions

One purpose of a proportional dimension performance algorithm may be toestimate length in either relative or absolute units to measure thingsdistinguished on a displayed visible or infrared image. Absolute unitsmay be derived using focal distance or proportioned from a distinctiveitem of known dimension which is either already in the image or isplaced there for this purpose.

For example in FIG. 2, illustrating an image 110 displayed in a portableinfrared camera system, if there is a first object 111 in the field ofview. First object 111 is defined by a first thermal zone 116 having aboundary 118 with at least one known dimension, L₁, between a firstpoint 120 and a second point 121 on the boundary 118. The portion of theimage between the first point 120 and the second point 121 covers apixel count P₁. There is also a second object 112 in the same or asimilar field of view. The second object 112 is defined by a secondthermal zone 122 having a boundary 124. Second object 112 has an unknownlength L₂ that spans a distance between third point 126 and fourth point128 on boundary 124. The portion of the image between the third point126 and the fourth point 127 covers a pixel count P₂. Pixel counts P₁and P₂ are examples of “image expanses.” An image expanse is a featureinherent in an image that defines spatial relationships in the image. Aperformance algorithm may be used to calculate the dimension L₂, usingthe following equation: L₂=L₁×(P₂/P₁). Note that the first and secondobjects 111 and 112 do not have to be in the same field of view at thesame time, but they need to be at roughly the same distance from theimager and at similar zoom settings for the calculation to be accurate.Note also that a reference thermal object of known dimensions may bespecifically placed into the field of view by the operator in order toassist with calculation. Objects 111 and 112 may be distinguished usingthe infrared imager, or objects 111 and 112 may be distinguished using avisible imager that is combined with the infrared imager in a portableinfrared camera system, or objects 111 and 112 may be distinguished byboth the infrared imager and the visible imager. If object 111 has beenspecifically placed in the field of view by the user to assist indimensional calculations, then object 111 may include identifying ororienting features such as notches or holes or tabs or pointers toassist the user with identification, location, orientation, andmeasurement.

Furthermore, if two objects such as 113 and 114, are placed in or foundin the image and are recognizable in the image, then the spatialrelationship between these two objects may be used to compute dimensionand coordinates for other things, particularly in that focal plane. Inaddition, a characteristic shape of and size, or temperature, or surfaceemissivity may be used in the following ways.

Know values for characteristic shape and size of an object in the image,such as that represented by either of the pentagonal configurationdisplayed for 113 and 114 in FIG. 2, may be used to establish theorientation and dimension for at least that portion of the image.Characteristic temperature may be used to distinguish objects 113 and114 from other things in the image. The operator who places theseobjects into the field of view may initiate warming or cooling them forthis purpose. Characteristic emissivity which is substantially differentfrom its background may be used to differentiate objects that are all atthe same temperature, when viewed through an infrared imager. However,the operator should be particularly careful with very low emissivitymaterials like polished metal because these surfaces often reflectinfrared energy quite well, and this should be considered whendistinguishing the objects 113 or 114.

For another example, if one may establish the field-of-view-anglerepresented by one pixel or group of pixels, and if one may estimate therange to the object of interest, then the proportional length, L₂, of anobject may be estimated from this equation: L₂=Cosine (angle)×range. Therange may be visually estimated by the operator, it may be measured, itmay be approximated using the focus setting, or it may be derived byanother means. Often times, the lower edge of the image is closer to theimager than the upper edge. In this case it is appropriate to take theparallax view into consideration, recognizing the trapezoidal arearepresented by an image.

EXAMPLE 2 Areas and Volumes

Another purpose of a proportional dimension performance algorithm may beto use length information to estimate area, perimeter, or volumeassociated with an observed phenomenon relevant to performance of objectbeing inspected. In one embodiment, a known (reference) object isselected to be three dimensional in nature, so as to already indicatethe viewing angle. The known object may be composed of materials withvarious infrared properties to provide perspective to the image toimprove the estimation of lengths, areas, and other physicalcharacteristics. For example, FIG. 3 illustrates a reference thermalobject 130 that has four surfaces 132, 134, 136, and 138 that may bedistinguished from background when using an infrared imager. Thisdistinction from background may be made using temperature differences,emissitivity differences, or a combination.

Object 130 in FIG. 3 is somewhat like objects 113 and 114 in FIG. 2because it has characteristic shape and size to provide orientation anddimension reference for things in the image. Moreover, object 130 hasthree-dimensional aspects which give depth orientation to things in theimage. If control markings like those shown in horizontal and verticaldirections on various sides, inside and outside, of object 130, may bedistinguished, then all things in the vicinity of object 130 may beinterpreted in respective proportions and orientation. If the detailmarkings cannot be distinguished, then at least the three-dimensionaloutline of object 130 may be distinguished and interpreted eitherautomatically by the programmed imager or manually by the in-fieldoperator. Furthermore to the extent that the temperature of eitherobject 130 in FIG. 3 or of objects 113 and 114 in FIG. 2 are atequilibrium with the surroundings; then by knowing the actual emissivityof a surface on one or more of those objects, one may interpret therelative emissivity of other surfaces at the same temperature in thefield of view.

FIG. 4 depicts a simplified image 150 from a thermal imager. Image 150has been converted from a color thermal image to a traditionalblack/white patent line drawing for purposes of illustration here.Analysis of image 150 by a performance algorithm is enhanced by theinclusion of two reference thermal objects 152 and 154. Referencethermal objects 152 and 154 each have known dimensions that facilitatecalculations of features related to a motor 156 that is portrayed inimage 150.

In short, the thermal and profile aspects of a known object may be usedto characterize the thermal and spatial aspects of other things in afield of view.

For certain performance algorithm calculations it is helpful for theuser to enable the imager to outline the vicinity of an area. Thisoutline is created to designate an area associated with a particularcharacteristic which is distinguished from the adjacent area(s).

Thermographic isotherm lines may be used to create an outline of thevicinity of an area on an infrared image. In some cases imagersautomatically create isotherm or similar color contour lines based onpreset limits. In other cases, the operator selectively controls thecreation of isotherm line or similar color contour mapping with theintent of marking off a desirable vicinity of an area as distinguishablefrom another.

Visible images may sometimes be superimposed on or mapped in correlationwith or correspondence to associated infrared images. Such mapping is anexample of “coordinating” two images. In some cases, the operator mayselect an optically distinguishable characteristic to indicate thevicinity of an area for consideration. For example a window may bediscernable from the wall in which the window is located. Firmware inthe imager is sometimes used to assist the operator with creating anoutline around an object like a window or a living thing or acolored-differentiated thing or anything distinguishable from adjacentor background items. In this way, either the visible image, or infraredimage, or both could be used to create an outline which is thenavailable for use as it is and for further processing.

EXAMPLE 3 Detection of Anomalies Using Calculations Within an Area

For areas defined as above, it may be of interest to determine themaximum, minimum, simple or weighted average of a parameter containedwithin a defined area. This could be used in reference to thetemperature profile of an object or to create an average of some derivedparameter over the area. The values created by the applied averagingtechnique could then be appropriately labeled, stored, and displayed asa trend over time. Using this approach, it would be possible for theuser to conduct regular scans of an object of interest, use thisinvention to establish dimensions and areas, derive averaged parameterswithin the area, and trend these parameters against some thresholdvalue. As long as the derived parameters do not exceed the predeterminedthreshold, then the user does not need to analyze the image. If athreshold is exceeded, then the camera or associated software maygenerate a user alert indicating that the relevant image of the objectof interest should be examined by the end user.

EXAMPLE 4 Insulating Value

Another purpose of a proportional dimension performance algorithm may beto estimate the effective insulating R-value, or the change in effectiveinsulating R-value for at least a portion of a wall using informationobtained from an image such as gaps in insulation, wet insulation,incomplete coverage, etc. FIG. 5 presents a schematic top view of asection of a wall 170. Wall 170 includes studs D that have a spacingF_(C) and a thickness F_(D). A first front wall covering A, a secondfront wall covering B, and a back wall covering E are provided.Insulation C fills the space between the studs D and the second frontwall covering /B and the back wall covering E. The studs D, the firstfront wall covering A, the second front wall covering B, the back wallcovering E and the insulation C each have insulative (R-) values R_(D),R_(A), R_(B), R_(E), and R_(C) respectively. Equation 1 may be used toestimate the potential R-value for a wall.

R _(eff) =R _(A) +R _(B+)(1/((f _(C) /R _(C))+(f _(D) /R _(D))))+R_(E)  (Eq'n 1)

where f_(C)=F_(C)/(F_(C)+F_(D)), and f_(D)=F_(D)/(F_(C)+F_(D))

Image information may be used to determine if the potential R-valueshould be depreciated to due to actual structural conditions. Foranother example, referring to FIG. 6, image 180 illustrates an elevationview of a wall of a building where the fiberglass insulation in the wallhas settled, leaving a gap at the top. The proportional area of settlingmay be estimated. By selecting an outline such as an isotherm line, theoperator may outline the portions of the image, S₁, S₂, S₃, and S₄ asillustrated in FIG. 6. The sum of these four areas divided by the totalimage may be one way to approximate the proportional area which isallocated to the un-insulated portion of the wall. Just as the equationin the previous paragraph may be used to estimate the effective R-valuefor a stud wall, the same calculation may be used to determine theeffective R-value for the wall with settled insulation, however, thistime using an adjusted value for Rc, allowing for missing insulation.

The adjusted value for Rc=(1/((fS/Rcv)+((1−fS)/Rcf))) where  (Eq'n 2)

-   -   fS=fraction of all sections “C” represented by S1+S2+S3+S4,    -   Rcv=R-value of section “C” void of insulation    -   Rcf=R-value of section “C” with fiberglass        In this example, the operator may utilize a user interface in        the portable inspection apparatus to select or enter normal        value for Rcf (e.g., the R-value for the insulation that is        expected to be found in a wall) and depreciated Rcv (e.g., the        R-value for an air pocket) for use in Equation 1 and Equation 2.

FIG. 7 presents a schematic representation of an image 200 of a roof,the image being produced by an infrared imager. Image 200 may be used todiscern the areas of a roof into which water has saturated roofinsulation. Image 200 depicts a dry area D of the roof and two wet areasW₁ and W₂. In a typical embodiment the operator outlines portions of theroof which show a significant temperature transition due to moisture ormissing insulation or gaps through which air exchange is taking place.Once again, outlines such as contour lines may be used to isolate thearea of interest into which, in this case, water has soaked. In the caseof wet insulation, the proportional area of a wet region compared to thefull area may then be used to calculate effective R-value for the roof.

Reff=(1/((fH2O/RH2O)+((1−fH2O)/RR))) where  (Eq'n 3)

-   -   fH2O=fraction of wall that is wet and is represented by W1+W2,    -   RH2O=R-value of the wet roof areas, and    -   RR=R-value of the dry roof areas.

The following is an implementation of Example 4 using an infrared imagerwith user interface limited to power on, power off, and display imageadjustments. In this case the imager is typically dedicated to roofinspections and is programmed with firmware that is configured to findwet insulation. This firmware allows the operator to adjust thesensitivity represented on the display and apply a custom pallet so thatthe operator performing a roof inspection may highlight wet insulationusing blue color over an otherwise grayscale background. The imager isfurther pre-programmed with expected R-values for wet and dry roofinsulation, such as RR=50, and RH2O=20. The programmed imager is used inthe field by an inspector who adjusts the image controls so that theimager displays meaningful information such as proportions for W1 and W2compared to roof area D. The application firmware in the imager displayshighlights over the thermally dissimilar wet areas using blue color,while displaying the remainder of the image in gray-scale. The inspectormakes final adjustments to image contrast using his observations andpersonal experience and determines that the image on the display isrealistic representation of wet in proportion to dry roof areas.Satisfied with this representation he notes an Reff value reported onthe display. For example if W1+W2 compared to D yields fH2O=0.3, thenReff=(1/((0.3/20)+((1−0.3)/50)))=34.

The following is a second implementation of Example 4 using an infraredimager with more sophisticated user interface such as that disclosed byPiety in '117, Hamrelius in '824, and Garvey in 20060017821. In thiscase the imager may have multiple functionalities, in which case theuser may select roof inspection, may enter or select the RR and RH2Ovalues and produce the same result as in the previous example.Furthermore, the inspector may make use of thermal image analysistechniques such as thermal contours to assist with the outlining ofareas W1 and W2. With the extended in-field user interface the operatorhas greater flexibility to perform a variety of functions.

An extension of this second implementation of Example 4 is to enable thein-field user to select from a series of algorithmic relationships oreven to construct a desired algorithm while in the field. Furthermorethe inspector with extended user interface may save images and includeinformation from multiple images in the computations, therebyrepresenting greater areas than one may capture in a single field ofview.

EXAMPLE 5 Fire Characteristics

Another purpose of a proportional dimension performance algorithm may beto estimate flame length, rate of spread, flashover or flameover, andother fire characteristics using the length and temperature informationfrom the infrared imager. FIG. 8 portrays an image 220 composed in adisplay in a portable inspection apparatus. Area T₁ is an image of afire taken at a first instant in time and area T₂ is an image of thesame fire taken at a second instant in time, while keeping the imagearea frame of reference relatively consistent between the first and thesecond instants in time. Proportional dimension calculation techniquesdescribed previously herein are used to determine flame length L_(fl1)and L_(fl2) and flame displacement L_(fs) in FIG. 8. Typically thelength is measured on the display using a mouse to drag a representativedistance across the frozen or dynamic infrared images. The operator maycalculate the flame spread rate, F_(s) using Equation 4.

F _(s) =L _(fs)/(T ₂ −T ₁)  (Eq'n 4)

L_(fs) in Equation 4 may be estimated from the information captured overtime using the infrared imager.

Warnings regarding potential flashover, flameover, or rollover may savefirefighters lives. Although it is very difficult to predict the veryrapidly spreading flashover or flameover conditions that occur whencombustible gases accumulate and ignite along walls and ceilings. Theinfrared imager may be used to warn firefighters that evidence may bedetected of two events that sometimes precede flameover or flashover:heat and rollover. When the gasses in the upper portions of a confinedspace accumulate smoke and extreme heat, the flameover or flashover mayoccur. Rollover is defined as sporadic flashes of flame mixed with smokeat a ceiling. Analyzed infrared information used in typical embodimentsincludes the use a vertical temperature gradient to mark off thevicinities where temperature is in range for flashover.

FIG. 9 portrays a schematic representation of an image 240 that may bepresented in the field to an operator of a portable inspection deviceusing an infrared imager to inspect the interior surface of a wall suchthat the upper portion of the image is the upper portion of the spaceand the lower portion of the image is the lower portion of the space. Atemperature gradient 242 is portrayed on the Y-axis and the X-axis isthe horizontal extent of a visual image, in this case a wall. When thetemperature gradient 242 spans a very large range from bottom to topsuch as 300° F. at the bottom and 2000° F. at the top, then there is avery high probability of flashover. Flashover is defined as thetemperature point at which the heat in the area or region is high enoughto ignite all flammable materials simultaneously. In this example ifoxygen is introduced to the upper space then combustion would probablybe immediate and violent. The imager may be programmed with additionalinformation interpreting the flashover potential and displaying ahorizontal flashover line 244 at a threshold temperature value as wellas a text and audio warning of impending danger. Threshold temperaturevalue is typically a material property such as flash point selected torepresent materials in the upper space. Portions of the display that arein region 246 of image 240 are above the flashover temperature andportions of the image that are in region 248 of image 240 are below theflashover temperature. Calibrated-temperature-measuring infrared imagersare preferred for this application. However, it is expected thatimager-only (e.g., thermal imagers that are not calibrated to measureabsolute temperature) imagers may also be programmed in a way thatidentifies the vicinity of an extreme-temperature-gradient around whichflashover is a potential risk or in another way that assists the firemanto provide early warning of this serious safety risk.

FIG. 10 illustrates a schematic view of an image 260 from an infraredimager being used to inspect a ceiling 262 such that the upper portion,lower portion, left side, and right side of the image are all ceilingsurfaces. A difference between FIGS. 9 and 10 is the orientation of thecamera has changed from horizontal in FIG. 9 to vertical in FIG. 10. Theimager may incorporate a performance algorithm to notify the operator ofa possible rollover location where the entire image is near flashovertemperature, but one area displays high temperature excursions of atransient nature. Rollover is defined as sporadic flashes of flame mixedwith smoke at a ceiling. In FIG. 10 the temperature of the entireceiling 262 is high. One local area 264 s seen to rollover in a fourstep sequence, starting with hotspot area 266 that expands to hotspotarea 268 that expands to hotspot area 270 that then collapses to hotspot area 272.

Performance algorithms in imagers may be used to determine theproportion of area in which hotspots are found. After the fire hasburned out, then firefighters perform overhaul. In this stage they lookfor hot spots with the intention of poking into the hot spot, applyingwater, and cooling down the hot spots, thereby speeding up thecompletion of the job. A performance algorithm may be used in the imagerto calculate the proportion of area surveyed covered by hotspots. To dothis, the imager typically uses a threshold hotspot isotherm level andcalculates the relative proportion of area included inside theisotherm(s) compared to the remainder of the image such as according toEquation 5.

Overhaul Remaining=Hot Spot portion of the image/Total image  (Eq'n 5)

The calculation may be instantaneous for an image or may be anaccumulation for an integral area larger than the field of view.

The following is implementation of Example 5 using an imager similar tothat disclosed by Warner. In '849 Warner describes a scheme ofhighlighting portions near human body temperature in a manner that isvisually distinct from all other portions of the image. In a similarmanner thermal information may be analyzed to highlight hot spotportions of the image. Exemplary embodiments significantly extend thisrudimentary concept wherein the imager is further programmed tocalculate the area represented by the hot spots in the image divided bythe total image area. This ratio is displayed as a value of overhaulremaining on the imager display.

EXAMPLE 6 Valve Performance

Another purpose of a proportional dimension performance algorithm may beto estimate the amount of material lost through a faulty steam trap orother type valve. These losses are calculated using ideal gas laws,steam tables, thermodynamics, fluid dynamics, or other equations basedon physical principles appropriate for the particular application. Toperform these calculations, one skilled in the art must gather specificinformation about the gas or fluid, the system pressure and temperature,and the application geometry and orifice characteristics. A typicalembodiment employs simplified calculations in the portable infraredcamera, and uses information collected in the field to make in-fieldcomputations. This normally requires compromise wherein grossassumptions are made in order to get an approximate result even thoughsome elements of specific information are yet unknown.

For example, the firmware in an infrared camera may be used to estimatethe losses from a faulty steam trap in the following manner. First,prompt the user to select the type trap from the following list:thermostatic steam traps, mechanical steam traps (includingfloat-thermostatic trap, inverted bucket trap), thermodynamic steamtraps, and assume mechanical steam trap with float-thermostatic trap asdefault. Prompt the user to determine if the trap has failed using theinformation seen on the infrared imager. Then assume an orifice diametersize of 7/32″ (or 0.218 inch) which would pass 24 lbs/hr@5 psig as thisis a typical situation. Next prompt the user asking at what percent opendid the trap fail, accepting 50% as the default if a particular answeris not given. Next, prompt the user to input how many hours per day thetrap encounters steam, accepting 12 hours per day as default. Nextprompt the user to input how many days per year the trap encounterssteam, accepting 25% (or 91 days) as the default. This information maybe used to derive an estimate of 24 lbs/hr steam loss. At $7.50 per 1000lbs, this amounts to $197.38 per year. This information might bedisplayed as image 280 on an infrared imager as follows shown in FIG.11.

An alternate performance algorithm may simply calculate typical valuesin mass and dollars for a steam trap failed in the open position; thensimply allow the user to determine the condition of the steam trap orother type valve using information from the infrared imager, and thenrecord these values in the imager memory for later accumulation andreporting purposes.

EXAMPLE 7 Gas Emissions

Another purpose of a proportional dimension performance algorithm may beto estimate a performance parameter associated with gas emissions suchas dynamic volume of gas cloud detected with imager sensitive tofugitive hydrocarbon or other gas emissions. Certain infrared imagerssuch as the GasFindIR by FLIR are designed to detect the presence ofparticular hydrocarbon gas emissions. By using the proportionaldimension capability of the present invention, the software may be usedto calculate the relative or absolute cross sectional representing thevolume of gas emission.

For example, FIG. 12 presents a schematic view of a an image 300 from aninfrared imager showing a gas cloud 302 escaping from a chemical processfitting 304. By measuring the horizontal 306 and the vertical 308distances representing the approximate boundaries of the gas cloud 302,one may use Equation 6 to approximate the volume of space around theleak where the gas is at a concentration higher than the minimumdetectible limit, MDL, for the imager in association with the particulargas.

V _(MDL)=(width+height)/2)³×(4/3)×(Pi)  (Eq'n 6)

The value, V_(MDL) may be used directly as a relative measure of thesize of one gas leak compared to another. On the other hand, one mightperform empirical testing or theoretical calculations to approximate thevolumetric rate of gas leaking, either at standard temperature andpressure or at other conditions that will sustain this size detectiblegas leak and thereby calculate a dollar value of the gases lost.

The estimate of the volume of space around the leak may be improved ifthe infrared camera operator is able to input particular informationabout gas pressure inside the system or about the nature orifice throughwhich the gas is flowing. Another extension of this volume calculationis the identification of the vicinity of the leak wherein it isparticularly dangerous to ignite a spark so as to avoid fire orexplosion. It is anticipated that, knowing the minimum detection limitfor a particular hydrocarbon, and knowing the fuel to air rationecessary to support combustion, one could program the infrared camerato distinguish and highlight an area on the image where spark ignitionis particularly risky. This may be inside or outside the outline of thegas on the image depending on the particular gas and detection limits.

After a leak is found and sized, it is meaningful to verify thecompleteness of the repair that has been done to correct the leak. Thepresent calculation of gas volume may be used to quantify theimprovement or reduction in leakage rate as a matter of proportion.

Another performance algorithm may be used to assist the user withidentifying locations around the gas leak where safety precautions areparticularly applicable. For example, the firmware may prompt the userto identify a contour line representative of the detected hydrocarbongas cloud. Then if the minimum detection limit for the particularhydrocarbon gas is smaller than or greater than the critical fuel-to-airratio needed to support combustion, the firmware may be programmed toestimate the position of a second contour line, either inside or outsideof the first, approximating the volume of space where ignition in thepresence of a spark source is most likely.

EXAMPLE 8 Electrical Faults

Another performance algorithm may use delta-T or severity to estimatetime-to-failure or safe distance or another performance parameterassociated with electrical faults. Electrical faults are normallydetected using infrared imaging based on the elevated temperatures dueto resistance heating in the vicinity of the fault. The severity of thefault is often directly in proportion to the delta temperature (e.g.,differential temperature) between faulty components and normalcomponents. The severity limit for an electrical fault is oftenreflected as a delta-T alarm on the display. If one uses a 20° C. limitfor high fault then the operator may designate an abnormal or faultycomponent with a first cursor and identify a normal component with asecond cursor. The infrared camera then displays the delta-T and reportsan alarm compared to the 20° C. limit. The present invention goesanother step and calculates such things as time-to-failure or safedistance based on information programmed into the in-camera firmware andreports the finding on the display. For example, if the actual delta-Tis 60° C., compared to high alarm delta-T of 20° C.; and if the limitlife of the component at high alarm is 180 days, then the firmware mayuse Equation 7 to estimate time-to-failure:

Time to failure=((high alarm delta-T)/(actual delta-T))̂3*(limit life),or

Time to failure=(20/60)̂3*180=7 days.  (Eq'n 7)

For another example the delta-T or severity information may be used todetermine a risk factor which in turn is used to calculate and displaysafe distance for the operator in vicinity of an identified electricalfault. In this case the operator determines (A) the presence ofelectrical fault, (B) the severity of that fault based on factors suchas delta-T and voltage, (C) component or equipment type, and inputs thisinformation the infrared imager user interface. The in-camera firmwareuses programmed rule-based logic to determine safe distance based fromthose inputs.

As another example, Equation 8 may be used to scribe a radiusrepresenting safe distance based on NFPA 70 E formula to calculatearc-flash boundaries.

D _(c)=(2.65*MVA _(fb) *t)̂½, where  (Eq'n 8)

-   -   Dc=distance in feet from an arc source for a second-degree burn    -   MVA_(fb)=bolted fault capacity in mega volt-amperes available at        the point involved−a function of available short circuit        current, and    -   t=time in seconds of arc exposure        Two particularly practical ways to use Equation 8 in the imager        are to either assume values for MVA_(fb) and t, or prompt the        user to input values for MVA_(fb) and t.

Yet another example using delta-T information in association with anelectrical fault calculation is to derive increased contact resistance.In this case an inspector uses a programmed infrared imager capable ofmeasuring delta-T information and calculating contact resistance. Theoperator inspects a series of contacts operating normally and identifiesa normal delta-T for those contact of 10 degrees C. above ambient. Theoperator logs this information into the imager and selects the specifiedcontact resistance for these contacts such as 3 ohms. During theinspection the operator finds one particular contact with normal loadingconditions but higher than normal delta-T value of 30 degrees C. aboveambient. The programmed imager uses the relationship P=I² R, where P ispower, I is current, and R is electrical resistance. I may be assumed tobe constant, provided the resistance change in the contact is a verysmall portion of the total resistance loading considering the drivenequipment on the power line. Therefore the programmed imager uses thefollowing relationship to calculate and display resistance valuesassociated with the observed temperature excursion:

Rc=Calculated resistance=Rn×((dTc−dTn)/dTn), where

-   -   Rn=Normal or specified resistance=3 ohm    -   dTn=delta-T above ambient for contact with normal or        as-specified electrical resistance    -   dTc=delta-T above ambient for contact with abnormal thermal        excursion.    -   Therefore, Rc=3×((30−10)/10))=9 ohms.    -   After calculating this result, the imager displays the following        information:        -   “Normal specified resistance=3 ohms        -   Estimated resistance for this connection=9 ohms, 300% above            specified value.”

Other embodiments using delta-T information to calculate power loss andderive associated physical property values as applicable may beextrapolated. Impedance losses often produce electrical heating inproportion to the impedance values as in the aforementioned caseregarding electrical resistance. Other impedance loss mechanisms such asmechanical friction also produce thermal heating in which the increasedtemperature is in proportion to the increased mechanical resistance orfriction coefficient.

EXAMPLE 9 Cash Value

Another purpose of a proportional dimension performance algorithm may beto estimate the dollar value that one could apply to any of the abovecalculations. One of the most useful calculation algorithms that may beused in an infrared imager is one that estimates cash value (e.g., costavoidance or cost savings or actual cost incurred). These calculationstypically are accomplished by using the infrared imager in the field tofind or verify the existence of a problem. Then the in-camera firmwareprompts the user to provide information to allow the in-camera computerto calculate cash value. An example is given for the steam trap in theforegoing case where 24.1 lbs/hour loss was determined, the steam trapencounters steam for 12 hours per day and 91 days per year, and the costof steam is $7.50 per 1000 lbs of steam. Therefore the annual cost forthis failed trap is estimated to be 24.1×12×91×7.50/1000=$197.38 peryear.

For another example, the infrared imager may be used to detect anelectrical connection that leads to switchgear providing primary powerto a manufacturing facility. Upon recording this entry in the infraredimager the firmware may prompt the user through information on thedisplay to estimate the following (example responses are in capitalletters beside each prompt):

-   -   i. Could this event prompt a production outage? YES    -   ii. Can such an outage be avoided by implementing a planned and        scheduled repair? YES    -   iii. What is the hourly cost for an unplanned outage? $17,000    -   iv. How long will it take to repair an unplanned failure of this        type? 16 HOURS    -   v. What is the repair cost if this is an emergency repair?        $10,000    -   vi. What is the repair cost if this is a planned and scheduled        repair? $5,000        Equation 9 may then be used to calculate the cash value.

Calculated cashvalue=(A×B)+C+D+E−F=(17,000×16)+10,000−5,000=$277,000  (Eq'n 9)

EXAMPLE 10 Rate of Change

Another purpose of a proportional dimension performance algorithm may beto estimate the change-over-time for a thermal or geometric aspectdistinguished with the imager. An example of this is already describedabove considering fire calculations at first and second points in time.Rate of change calculations are important for in-field infrared imagingbecause these enable the technician using the camera to have real-timeinformation helping him or her to do their assignment with greateraccuracy and knowledge. This plays an important role in manyapplications where transient thermal conditions are experienced, andsince heat transfer is frequently not in a steady state condition, thereare nearly limitless applications where rate of change calculations maybe employed. Keep in mind however that this is different fromtraditional trending where data is collected periodically and uploadedto a desktop computer with historical database. In this case thetechnician in the field collects first and second measurements and makesa rate of change calculation while in the field.

For another example, the user of this feature typically desires toestablish a steady field of view, which is easily done by mounting theimager or holding it steady. Then the technician triggers the transientcapture mode. When this is done the firmware in the camera baselines theimage by recording the temperature values for every pixel, then itautomatically tracks one or all of the following:

-   -   change in maximum temperature for all pixels,    -   change in minimum temperature for all pixels, or    -   change in each pixel over a designated time interval.        Typically the designated time interval is the period between        first and second button clicks. The display may be used to        report the delta-temperature for maximum and minimum pixel        values along with a gray-scale or color pallet delta-temperature        map, similar in function to the thermal image, yet very        different in appearance. One may choose to overlay a partially        transparent representation of the delta temperature image over        the baseline temperature image. There are many other variations        to this format which may be employed to fit a particular need or        user preference.

EXAMPLE 11 Other Calculations

There are many other calculations using performance algorithms that maybe done in the manner of the previous examples. For instance one mayapply the proportional dimensions calculation to estimate the physicaldimensions of a fault that could be found in a furnace, boiler or otherrefractory structure. In the same way one may apply thermal insulationheat transfer calculations to these applications and others. Furthermorethe calculation of thermal efficiency, power, friction losses, are justa few of the many other algorithmic calculations that may also beimbedded into firmware and used to prompt infrared camera operators toperform better inspections, analyses, and reports while in the field.

EXAMPLE 12 Auto-Scale Compensation

Many infrared imagers convert thermal emissivity information to colorimages where portions of the image that are relatively hot are portrayedin red tones, portions of the image that are relatively average areportrayed in green tones, and portions of the image that are relativelycool are portrayed in blue tones. In order to highlight the relativedifferences many infrared imagers incorporate a feature called“auto-scaling.” With auto-scaling, the highest temperature regions in animage is portrayed as red, the average temperature regions are portrayedas green, and the lowest temperature regions are portrayed as blue,regardless of absolute temperatures. This in image of objects ranging intemperature from 70 to 80 degrees portrayed with auto-scaling may havethe same color depictions as an image of the same objects ranging intemperature from 75 to 85 degrees or from 80 degrees to 120 degrees.Auto-scaling is beneficial to the extent that it maximizes the portrayalof temperature differences (i.e., it maximizes temperature resolution),but it is detrimental to the extent that it may cloak important absolutetemperature excursions.

For example, FIG. 13A depicts a simplified image 320 from a thermalimager. Image 320 that has been converted from a color thermal image toa gray-scale image for purposes of illustration here. FIG. 13A depicts amotor 322 inspected at a first date, the image showing a minimumtemperature of 75° F. and a maximum temperature of 135° F. The imagerwas set to auto-scale from 75° F. to 135° F. FIG. 13B depicts asimplified image 330 of from a thermal imager taken at a second date sixmonths after the first date. Image 330 showing a minimum temperature of85° F. and a maximum temperature of 182° F., and the imager was set toauto-scale from 75° F. to 135° F. FIG. 13A and FIG. 13B are visuallysubstantially the same, a similarity that also exists in the originalcolor thermal images that were used to create FIGS. 13A and 13B.

FIG. 13C depicts an image 340 that has been modified by a performancealgorithm to compare the image 320 of FIG. 13A with the image 330 ofFIG. 13B, removing auto-scaling adjustments and highlighting an area 342of the image that are above a threshold temperature of 135° F. or someother determined threshold based on reference image. This highlightingof area 342 by the performance algorithm helps alert the operator thatsomething has changed between the two images.

FIG. 14 illustrates a flow chart 400 of a method embodiment. In step402, a thermal zone is defined in an image, the thermal zone having aboundary. In step 404, a first image expanse is determined between afirst point and a second point on the boundary of the thermal zone, thefirst point and the second point being spaced apart a known referencedistance. Step 406 includes determining second image expanse between athird point and fourth point spaced apart a first unknown distance inthe image. In step 408 a first estimated distance between the thirdpoint and the fourth point in the image is calculated using the knownreference distance and a ratio between the first image expanse and thesecond image expanse.

In summary, provided herein are systems and methods for enhancinginspections using infrared cameras through in-field displays andoperator-assisted performance calculations. A handheld infrared imagingsystem typically includes an infrared camera having a programmedcomputer (optionally with stored reference data from previous infraredor optical scans) and an interactive user interface suitable fordisplaying images and prompting response and accepting input from theinfrared camera operator in the field during an inspection. The userinterface may enable an operator to designate at least one thing ofinterest on a displayed infrared image; and the programmed computer usesa performance algorithm to estimate performance associated with thething of interest such as one of the following: adimensioned-measurement value or a thermal-property characteristic or anelectrical-property characteristic or a cash value or a rate-of-changecharacteristic or an assessment of risk or danger. The programmedcomputer may extract information or parameters from previously measureddata (either transferred down from the PC database or locally residentin the camera from previously stored measurements). The programmedcomputer may vary the way in which it displays new measurements,including live or continuously updated display, based on the informationextracted from the stored data. Implementations may incorporate a deviceused for periodic measurements or an “online” device used for continuousor continuously updated monitoring. One or more of the parametersextracted from the IR image may be trended or otherwise adapted toprovide an automated alert to the user regarding significant changes orimportant findings.

The foregoing descriptions of embodiments of this invention have beenpresented for purposes of illustration and exposition. They are notintended to be exhaustive or to limit the invention to the precise formsdisclosed. Obvious modifications or variations are possible in light ofthe above teachings. The embodiments are chosen and described in aneffort to provide the best illustrations of the principles of theinvention and its practical application, and to thereby enable one ofordinary skill in the art to utilize the invention in variousembodiments and with various modifications as are suited to theparticular use contemplated. All such modifications and variations arewithin the scope of the invention as determined by the appended claimswhen interpreted in accordance with the breadth to which they arefairly, legally, and equitably entitled.

1. A method for estimating a time to failure of a component using aprogrammable infrared camera having programmed logic, comprising: (a)inputting into the programmed logic a limit life for the component; (b)inputting into the programmed logic a high alarm delta-T for thecomponent; (c) measuring a first temperature of a faulty component withthe programmable infrared camera; (d) measuring a second temperature ofa normal component with the programmable infrared camera; (e)subtracting the second temperature from the first temperature to providea delta-T temperature in the programmed logic; (f) using the programmedlogic to divide the high alarm delta-T by the calculated delta-Ttemperature to obtain a ratio; and (g) using the programmed logic tomultiply the limit life by a function of the ratio to estimate the timeto failure of the component.
 2. The method of claim 1 wherein theprogrammable infrared camera has a display with a first cursor and asecond cursor, and step (a) comprises measuring the first temperature ofthe faulty component with the programmable infrared camera bypositioning the first cursor over a first image of the faulty componenton the display; and step (b) comprises measuring the second temperatureof the normal component with the programmable infrared camera bypositioning the second cursor over a second image of the normalcomponent on the display.
 3. The method of claim 1 where in step (e)comprises using the programmed logic to multiply the limit life by anexponential function of the ratio.
 4. The method of claim 1 wherein step(e) comprises using the programmed logic to multiply the limit life bythe ratio cubed.
 5. A method of advising a safe distance from anarc-flash hazard to an operator using a programmable infrared camerahaving programmed logic and a display, comprising: (a) programming theprogrammed logic with a formula employing at least one variable forcalculating a safe distance from an arc-flash source; (b) inputting theat least one variable into the programmed logic; (c) using theprogrammed logic to calculate the safe distance; and (d) displaying thesafe distance on the display.
 6. The method of claim 5 wherein theprogrammable infrared camera comprises a user interface for inputtingthe at least one variable into the programmed logic and step (b)comprises prompting the user to use the user interface to input the atleast one variable into the programmed logic.
 7. The method of claim 5wherein step (b) comprises inputting the at least one variable into theprogrammed logic as an assumed value.
 8. A method for estimating anactual electrical impedance of an electrical component in a systemdrawing an electrical current, the method using a programmable infraredcamera having programmed logic and comprising: (a) inputting into theprogrammed logic a normal impedance for the electrical component; (b)inputting into the programmed logic a normal above-ambient delta-T valuefor the electrical component; (c) using the programmable infrared camerato input into the programmed logic a measured above-ambient delta-Tvalue for the electrical component; (d) subtracting the normalabove-ambient delta-T value from the measured above-ambient delta-Tvalue to provide a delta-T variance in the programmed logic; (e) usingthe programmed logic to divide the delta-T variance by the normalabove-ambient delta-T value to obtain a variance ratio; and (f) usingthe programmed logic to multiply the normal impedance by the varianceratio, wherein the electrical current drawn by the system is assumed tobe a constant that is independent of the actual electrical impedance ofthe electrical component to estimate the actual electrical impedance ofthe electrical component.
 9. A method for estimating an actual frictionresistance of a mechanical component using a programmable infraredcamera having programmed logic, comprising: (a) inputting into theprogrammed logic a normal friction resistance for the mechanicalcomponent; (g) inputting into the programmed logic a normalabove-ambient delta-T value for the mechanical component; (h) using theprogrammable infrared camera to input into the programmed logic ameasured above-ambient delta-T value for the mechanical component; (i)subtracting the normal above-ambient delta-T value from the measuredabove-ambient delta-T value to provide a delta-T variance in theprogrammed logic; (j) using the programmed logic to divide the delta-Tvariance by the normal above-ambient delta-T value to obtain a varianceratio; and (k) using the programmed logic to multiply the normalfriction resistance by the variance ratio to estimate the actualfriction resistance of the mechanical component.